An optimal photon utilization is important for the economic performance of a photocatalytic reactor. However, for the desired reactor performance, it is often difficult to predict the required photon utilization. In this work, automated feedback and feedforward controllers are investigated to maintain the reactor conversion close to a desired value by adjusting the photon irradiance within a LED-based photocatalytic reactor for toluene degradation. The feedback controller was able to control the conversion during a set-point tracking experiment and was able to mitigate the effects of catalyst deactivation in an automated fashion. The feedforward controller was designed based on an empirical steady-state model to mitigate the effect of changing toluene inlet concentration and relative humidity, which were measured input disturbances. The results demonstrated that feedback and feedforward control were complementary and could mitigate the effects of disturbances effectively such that the photocatalytic reactor operated close to desired conditions at all times. The presented work is the first example of how online analytical technologies can be combined with “smart” light sources such as LEDs to implement automated process control loops that optimize photon utilization. Future work may expand on this concept by developing more advanced control strategies and exploring applications in different areas. @en

The design and operation of reactors for photocatalytic degradation of organic pollutants remains challenging due to the complex interplay of photon, mass, and heat transfer. An integrated process model including a radiation field, reaction kinetics, and material balances of an annular LED-based photocatalytic reactor for photocatalytic degradation of toluene is validated using experimental data from a mini-pilot plant. A particular emphasis is on the effect of water on reaction kinetics, toluene conversion, mineralization, and catalyst deactivation, which is currently not well understood. The results from parameter estimation demonstrate that a competitive reaction rate model describes the experimental data with varying water concentration best. Furthermore, experimental trends demonstrate that toluene conversion is highest at low water concentrations, however, mineralization and catalyst lifetime are enhanced by the presence of water. The validation of the integrated process model and understanding of the role of water allow for improved design and operation of future photocatalytic reactors.

@en